Delineating oil-sand reservoirs with high-resolution PP/PS processing and joint inversion in the Junggar Basin, Northwest China
Bibliographic record
Abstract
Dramatic lateral lithological variations in the fluvial sediments in Chepaizi, in the Junggar Basin of Northwest China, have posed challenges in distinguishing true and false “bright spots” in oil-bearing sand reservoirs. A high-resolution multicomponent seismic survey and joint prestack PP and PS inversion conducted recently provided an effective technique for solving the problem and successfully delineating the characteristics of the reservoir. This is due to the different reflection response of shear waves to the lithology and fluid content, and their ability to resolve thin layers. Surface-consistent amplitude- and resolution-preserving pro-cessing produced high-quality prestack PP and PS migrated gathers and stacks for extracting seismic attributes. Application of joint prestack PP and PS inversion resulted in higher fluid factors and lower VP/VS in oil-bearing sands compared with dry sands. The correlation between the inversion and the existing well data suggests that high-resolution multicomponent AVO can reduce drilling risks and provide more accurate reservoir characterization.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".